مدلسازی پاسخ جوانه‌زنی بذر علف‌های‌هرز جودره (Hordeum spontaneum) و علف قناری (Phalaris minor) به دما

نوع مقاله: مقاله پژوهشی


دانشگاه تهران


مدل‌های گوناگونی برای پیش‌بینی پاسخ جوانه‌زنی بذر به دما ارایه شده‌اند که مهمترین و پرکاربردترین آنها مدل زمان گرمایی (یکای گرمایی) است. این مدل اگرچه دارای ویژگی‌های زیست‌شناختی مناسبی برای جوانه‌زنی است ولی بر پایه پیش‌‌فرض‌هایی است که در برخی گونه‌ها (بویژه علف‌های‌هرز) دیده نمی‌شود که برآیند آن پیش‌بینی نادرست جوانه‌زنی است. در این مقاله مدلی بر پایه توزیع آماری ویبول پیشنهاد می‌گردد که نه تنها از نظر زیست‌شناختی بر مدل مرسوم برتری دارد بلکه پیش‌بینی بسیار مناسبی از الگوی جوانه‌زنی نیز فراهم می‌آورد. در پژوهشی آزمایشگاهی جوانه‌زنی بذرهای علف‌های هرز جودره (Hordeum spontaneum) و علف‌قناری (Phalaris minor) در دماهای 8، 12، 16، 20،‌ 24 و 28 درجه سانتی‌گراد آزمون شد و پاسخ جوانه‌زنی آنها توسط هر دو مدل توصیف گردید. مدل مرسوم برازش نامناسبی به داده‌های جوانه‌زنی دو گونه و بویژه علف‌قناری داشت (9 تا 12% خطا). حال آنکه خطای مدل پیشنهادی در این مقاله تنها 4% بود و پیشرفت جوانه‌زنی در طی زمان نیز به خوبی توسط مدل پیش‌بینی شد. مدل پیشنهادی همچنین به خوبی توانست سه پدیده زمان درنگ (زمان تا آغاز جوانه‌زنی)، ‌سرعت جوانه‌زنی و درصد جوانه‌زنی پایانی را در هر دو گونه برآورد نماید. جداسازی تاثیر دما بر سرعت جوانه‌زنی و جوانه‌زنی پایانی از ویژگی‌های مهم این مدل است که به اهمیت بوم‌شناختی آن می‌افزاید. برای نمونه،‌ دمای بهینه برای سرعت جوانه‌زنی در هر دو گونه (در جودره 8/21 و در علف‌قناری 5/23 درجه سانتی گراد) بالاتر از گستره دمایی بهینه بر حسب درصد جوانه‌زنی پایانی بود (در جودره بین 5/7 تا 20 و در علف‌قناری بین 5/7 تا 16 درجه سانتی‌گراد). این ویژگی سازش‌پذیری بسیار بالایی به هر دو گونه برای جوانه‌زنی در دامنه گسترده‌ای از دماهای محیطی می‌دهد.


عنوان مقاله [English]

Investigating Efficiency of Non-chemical Methods to Management of Weeds in Forage Sorghum (Sorghum bicolor)

نویسندگان [English]

  • Mohsen Beheshtian
  • Hamid Rahimain
  • Hassan Alizadeh
  • sara Ohadi
  • Ahmad Zare
چکیده [English]

Different models have been developed to describe the germination responses of seeds to temperature among which the thermal time (heat unit) model has received the greatest implications. Although biologically relevant, the thermal time model is confined to some assumptions which may not be met in some species (particularly in weed species) and thus can result in poor predictions. In this paper we address a novel Weibull-based model which is not only more biologically relevant but also provides the better predictions of germination compared to the conventional model. Therefore, in a laboratory experiment the seed germination of wild barley (Hordeum spontaneum) and little canary grass (Phalaris minor) was tested at various sub- and super-optimal temperatures including 8, 12, 16, 20, 24 and 28 oC. The both models were then fitted to the data and compared. The conventional thermal time model provided very poor fits to the germination data of both (particularly P. minor) (RMSE = 9% to 12%). However, the new model well fitted to the same datasets with only 4% error (i.e. RMSE). The Weibull-based model was also good at estimating the germination lag, germination rate and final percent germination in either of weeds studied. Separating the effect of temperature on germination rate and germination extent is suggested to be amongst the most significant ecological properties of the model. For example, the optimum temperature for the mid germination rate (21.8 oC in H. spontaneum  and 23.5 oC in P. minor) was found to be higher than and beyond the optimum range of germination extent (7.5 to 20 oC in H. spontaneum  and 7.5 to 16 oC in P. minor). This can give the two species a high degree of germination plasticity in response to the environmental temperatures.  

کلیدواژه‌ها [English]

  • Thermal time model
  • Weibull-based model
  • germination lag
  • Germination rate
  • germination extent
Alvarado, V. and  Bradford, K. J. 2002. A hydrothermal time model explains the cardinal temperatures for seed germination. Plant, Cell Env.t 25: 1061–1069.

Alvarado, V. and  Bradford K. J. 2005. Hydrothermal time analysis of seed dormancy in true (botanical) potato seeds. Seed Sci Res. 15: 77–88.

Baskin, C.C. and Baskin, J. M. 1998. Seeds: ecology, biogeography, and evaluation of dormancy and germination. San Diego: Academic Press.

Batlla, D. and Benech-Arnold, R. L. 2003. A quantitative analysis of dormancy loss dynamics in Polygonum aviculare L. seeds: Development of a thermal time model based on changes in seed population thermal parameters. Seed Sci Res. 13: 55–68.

Bradford, K. J. 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50: 248–260.

Brown, R.F. and Mayer, D.G. 1988. Representing cumulative germination. 2. The use of Weibull function and other empirically derived curves. Annals Bot. 61: 127-138.

Burnham, K.P. and Anderson, D.R. 2002. Model selection and multimodel inference: A practical information-theoretic approach. New York, USA: Springer.

Bury, K. 1999. Statistical distributions in engineering. Cambridge: Cambridge University Press.

Chhokar, R. and  Malik, R. 1999. Effect of temperature on germination of Phalaris minor Retz. Indian J Weed Sci. 31: 73-74.

Coles, S. 2001. An introduction to statistical modeling of extreme values. (Vol. 208). London: Springer.

Covell, S., Ellis, R.H., Roberts, E.H. and Summerfield, R. J. 1986. The influence of temperature on seed germination rate in grain legumes I. A comparison of chickpea, lentil, soyabean and cowpea at constant temperatures. J Exp Bot. 37: 705-715.

Dumur, D., Pilbeam, C. J. and Craigon, J. 1990. Use of the weibull function to calculate cardinal temperatures in faba bean. J Exp Bot. 41: 1423-1430.

Ekeleme, F., Forcella, F., Archer, D.W., Akobundu, I.O. and Chikoye, D. 2005. Seedling emergence model for tropic ageratum (Ageratum conyzoides). Weed Sci. 53: 55-61.

Ekeleme, F., Forcella, F., Archer, D.W, Chikoye, D. and Akobundu, I.O. 2004. Simulation of shoot emergence pattern of cogongrass (Imperata cylindrica) in the humid tropics. Weed Sci. 52: 961-967.

Ellis, R.H., Covell, S., Roberts, E.H. and Summerfield, R.J. 1986. The influence of temperature on seed germination rate in grain legumes II. Intraspecific variation in chickpea (Cicer arietitium l.) at constant temperatures. J Exp Bot. 37: 1503-1515.

Evans, M., Hastimgs, N. and Peacock, B. 2000. Statistical distributions. New York: John Wiley & Sons, Inc.

Fyfield, T.P. and Gregory, P.J. 1989. Effects of temperature and water potential on germination, radicle elongation and emergence of mungbean. J Exp Bot. 40: 667-674.

Garcia-Huidobro, J., Monteith, J. L. and Squire, G.R. 1982 Time, temperature and germination of pearl millet (Pennisetum typhoides S & H.). I. Constant temperature. J Exp Bot. 33: 288–296.

Gozlan, S. and Gutterman, Y. 1999. Dry storage temperatures, duration, and salt concentrations affect germination of local and edaphic ecotypes of Hordeum spontaneum (Poaceae) from Israel. Biol J Lin Soci. 67: 163-180.

Grundy, A.C., Phelps, K., Reader, R. J. and  Burston, S. 2000. Modelling the germination of Stellaria media using the concept of hydrothermal time. New Phytol. 148: 433-444.

Hardegree, S. P. 2006a. Predicting germination response to temperature. III. Model validation under field-variable temperature conditions. Annals Bot. 98: 827–834.

Hardegree, S. P. 2006b. Predicting germination response to temperature. I. Cardinal-temperature models and subpopulation-specific regression. Annal Bot. 97: 1115–1125.

Hardegree, S. P. and Winstral, AH. 2006. Predicting germination response to temperature. II. Three-dimensional regression, statistical gridding and iterative-probit optimization using measured and interpolated-subpopulation data. Annals Bot. 98: 403–410.

Kebreab, E. and Murdoch, A. J. 1999. Modelling the effects of water stress and temperature on germination rate of Orobanche aegyptiaca seeds. J Exp Bot. 50: 655-664.

Kebreab, E. and Murdoch, A. J. 2000. The effect of water stress on the temperature range for germination of Orobanche aegyptiaca seeds. Seed Sci Res. 10: 127–133.

Machin, D., Cheung, Y. B. and Parmar, M.K.B. 2006. Survival analysis: a practical approach. England: John Wiley and Sons.

Marshall, B. and Squire, G. R. 1996. Non-linearity in rate-temperature relations of germination in oilseed rape. J Exp Bot. 47: 1369-1375.

Meyer, S.E. and Allen, P.S. 2009. Predicting seed dormancy loss and germination timing for Bromus tectorum in a semi-arid environment using hydrothermal time models. Seed Sci Res. 19: 225–239.

Piper, E.L., Boote, K. J., Jones, J.W. and Grimm, S.S. 1996. Comparison of two phenology models for predicting flowering and maturity date of soybean Crop Sci. 36: 1606–1614.

Roman, E.S., Thomas, A.G., Murphy, S. D. and Swanton, C. J. 1999. Modeling germination and seedling elongation of common lambsquarters (Chenopodium album). Weed Sci. 47: 149-155.

Shafii, B. and Price, W. J. 2001. Estimation  of cardinal  temperatures  in germination  data  analysis. J Agri Biol Env Stat. 6: 356-366.

Soltani, A., Robertson, M. J., Torabi, B., Yousefi-Daz, M. and Sarparast, R. 2006 Modelling seedling emergence in chickpea as influenced by temperature and sowing depth. Agri Forest Meteorol. 138: 156–167.

Timmermans, B.G.H., Vos, J., van Nieuwburg, J., Stomph, T.J. and van der Putten, P.E.L. 2007. Germination rates of Solanum sisymbriifolium: temperature response models, effects of temperature fluctuations and soil water potential. Seed Sci Res. 17: 221–231.

Watta, M.S., Xub, V. and Bloomberg, M. 2010. Development of a hydrothermal time seed germination model which uses the Weibull distribution to describe base water potential. Ecol Model. 221: 1267–1272.

Weibull, W. 1951. Astatistical distribution function of wide applicability. J Appl Mech Trans Asme. 18: 293–297.

Yin, X., Kropff, M.J., McLaren, G. and Visperas, R.M. 1995. A nonlinear  model  for  crop  development  as  a function  of  temperature. Agri  Forest Meteorol. 77: 1-16.